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This study presents a pan-genome-based machine learning approach to predict antimicrobial resistance (AMR) in Escherichia coli strains. By analyzing core and accessory gene clusters, the researchers identified that specific AMR genes in the accessory genome significantly contributed to resistance predictions, outperforming established gene sets. The findings suggest the potential for using machine learning algorithms to enhance understanding of bacterial AMR mechanisms and improve predictions of resistant s

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